Facial Contour Labeling via Congealing

It is a challenging vision problem to discover non-rigid shape deformation for an image ensemble belonging to a single object class, in an automatic or semi-supervised fashion. The conventional semi-supervised approach [1] uses a congealing-like process to propagate manual landmark labels from a few images to a large ensemble. Although effective on an inter-person database with a large population, there is potential for increased labeling accuracy. With the goal of providing highly accurate labels, in this paper we present a parametric curve representation for each of the seven major facial contours. The appearance information along the curve, named curve descriptor, is extracted and used for congealing. Furthermore, we demonstrate that advanced features such as Histogram of Oriented Gradient (HOG) can be utilized in the proposed congealing framework, which operates in a dual-curve congealing manner for the case of a closed contour. With extensive experiments on a 300-image ensemble that exhibits moderate variation in facial pose and shape, we show that substantial progress has been achieved in the labeling accuracy compared to the previous state-of-the-art approach.

以自动或半监督的方式为属于单一对象类别的图像集合发现非刚性形状变形是一个具有挑战性的视觉问题。传统的半监督方法[1]使用一种类似凝固的过程将手动标记的地标从少数图像传播到大量图像集合。尽管在具有大量人口的人物间数据库上有效，但仍有提高标记准确性的潜力。为了提供高度准确的标记，在本文中，我们为七个主要面部轮廓中的每一个都提出了一种参数曲线表示。沿着曲线的外观信息（称为曲线描述符）被提取出来并用于凝固。此外，我们证明了诸如方向梯度直方图（HOG）等高级特征可以在所提出的凝固框架中使用，对于闭合轮廓的情况，该框架以双曲线凝固的方式运行。通过对300张图像集合进行大量实验，这些图像在面部姿势和形状上表现出适度的变化，我们表明与之前的最先进方法相比，在标记准确性方面取得了实质性进展。